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Future Internet 2019, 11(3), 68; https://doi.org/10.3390/fi11030068

Snack Texture Estimation System Using a Simple Equipment and Neural Network Model

Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan
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This paper is a revised and expanded version of a paper entitled “Texture Estimation System of Snacks Using Neural Network Considering Sound and Load” presented at The 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2018), Taichung, Taiwan, 27–29 October 2018.
Received: 28 December 2018 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed. View Full-Text
Keywords: food texture; neural network; human sensibility; artificial intelligence; CNN food texture; neural network; human sensibility; artificial intelligence; CNN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kato, S.; Wada, N.; Ito, R.; Shiozaki, T.; Nishiyama, Y.; Kagawa, T. Snack Texture Estimation System Using a Simple Equipment and Neural Network Model. Future Internet 2019, 11, 68.

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